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Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you...

129,191 просмотров • 1 месяц назад •via X (Twitter)

Комментарии: 34

Фото профиля Google AI
Google AI1 месяц назад

— Google AI Pro and Ultra subscribers can experience 3.7 Flash today via Spark in the @GeminiApp — Access the model in the Gemini Enterprise Agent Platform and Gemini Enterprise app — Build in the Gemini API via @googleaistudio and @androidstudio, and explore agent-first workflows in @antigravity — Learn more in the blog ↓

Фото профиля Teneo Protocol
Teneo Protocol1 месяц назад

@GeminiApp Cheaper intelligence becomes much more interesting when agents can actually act on it. Better planning, tool use, and lower inference costs could push agents from assistants toward real economic actors.

Фото профиля Vanar
Vanar1 месяц назад

@GeminiApp The more capable and affordable agents become, the closer we get to AI that can actually run workflows end to end.

Фото профиля throwaway_304
throwaway_3041 месяц назад

@GeminiApp My gf and I tried to use Voice mode earlier today and it was so incredibly bad. Definitely needs an update. Even regular 3.0 was better. We asked it to translate Chinese/English and it randomly spoke Korean and also just said a lot of nonsense.

Фото профиля MerkleFlow
MerkleFlow1 месяц назад

@GeminiApp Now that Sergey is back in a more extended capacity - we hope to see Google going back to the frontier race

Фото профиля Inspired Taste
Inspired Taste1 месяц назад

@GeminiApp All based and trained on immense amounts of theft…

Фото профиля EllyEleven
EllyEleven1 месяц назад

@GeminiApp This can be perfect workhorse model, And usage limits are good to in ai studio or use antigravity This is a perfect model for analysis and large document search. Or just quick easy prototypes.

Фото профиля ˚ sofi
˚ sofi1 месяц назад

@GeminiApp so the agents can do my to-do list while i scroll twitter? finally.

Фото профиля Wilson T.
Wilson T.1 месяц назад

@GeminiApp DeepSWE: 48.6% → 65.3% with $0.75/M input. Hmmm interesting

Фото профиля Kostya | AI
Kostya | AI1 месяц назад

@GeminiApp Gemini 3.7 Flash sounds like a notable step for AI‑assisted coding; it'll be interesting to see how its speed and agent capabilities compare to existing tools.

Фото профиля Knowix
Knowix1 месяц назад

@GeminiApp developers are eating good with this one, love it!

Фото профиля Brian Cheong
Brian Cheong1 месяц назад

@GeminiApp Most agent failures are product failures, not model failures. Scope, permissions, and handoff rules decide more than the benchmark score.

Фото профиля Anis🐬Al
Anis🐬Al1 месяц назад

Gemini 3.7 Flash represents a significant step forward in optimizing workflows for coding and complex agent tasks. Integrating this model into the Gemini app suggests a focus on enhancing personal AI productivity. How do you see this specific iteration impacting the speed of agent-led development?

Фото профиля Dmitriy King
Dmitriy King1 месяц назад

@GeminiApp AI agents are getting smarter (Gemini 3.7 Flash), now they need a clean chain to run on. Molum — L3 for AI agents, community-owned, no VC. The pack is building. $MOLUM

Фото профиля Rudy
Rudy1 месяц назад

@GeminiApp You didn't even mention the best app it works well with. Google keep

Фото профиля Utkarsh
Utkarsh1 месяц назад

@GeminiApp marketing team did its job well

Фото профиля JOKMAH | Inteligencia causal
JOKMAH | Inteligencia causal1 месяц назад

@GeminiApp “Smartest work model” will be decided outside the launch demo. Give every model the same repo, tools and budget; then report accepted patches, retries, broken tests and human corrections. Speed matters, but cost per verified outcome is the number agents are missing.

Фото профиля ToolRadarAI
ToolRadarAI1 месяц назад

@GeminiApp cool. now show tool logs and the invoice.

Фото профиля QuietLayer Studio
QuietLayer Studio1 месяц назад

@GeminiApp mate, cost is what matters now!! Heard of DeepSeek?

Фото профиля Edward Hernandez
Edward Hernandez1 месяц назад

@GeminiApp Gemini 3.7 Flash? Sounds like my new sidekick, but can it beat my procrastination?

Фото профиля Kostya | AI
Kostya | AI1 месяц назад

@GeminiApp Gemini 3.7 Flash sounds promising for coding assistants, but real‑world performance will depend on benchmarks and integration details that aren’t public yet.

Фото профиля 安叫兽|Bird🕊️ 🔶 BNB
安叫兽|Bird🕊️ 🔶 BNB1 месяц назад

@GeminiApp 任务清单能不能少,得先看它会不会自己加活。

Фото профиля sonil
sonil1 месяц назад

@GeminiApp gemini 3.7 flash looking like a solid workhorse for agents

Фото профиля Inflectiv AI ⧉
Inflectiv AI ⧉1 месяц назад

@GeminiApp Better planning and tool use matter more than raw benchmark gains when agents are handling real workflows.

Фото профиля sof c,
sof c,1 месяц назад

@GeminiApp Claiming 'most intelligent' without benchmarks is just marketing. How do you measure intelligence in coding-through raw speed, correctness, or adaptability?

Фото профиля zenramen
zenramen1 месяц назад

@GeminiApp 3.7 flash is a big jump, wonder how that's gonna play out in coding tasks

Фото профиля Sebastian Buzdugan
Sebastian Buzdugan1 месяц назад

@GeminiApp gemini 3.7 flash speed matters less when repo-wide edits still need regression tests

Фото профиля Miss HR | Technical Recruiter
Miss HR | Technical Recruiter1 месяц назад

@GeminiApp Impressive progress on agentic AI for real-world workflows, multi-step task handling with less oversight is exactly where the industry's heading.

Фото профиля Namujogo Brenda
Namujogo Brenda1 месяц назад

@GeminiApp Exciting advancements! AI models like Gemini 3.7 Flash can really enhance productivity, especially in automating tasks. When integrating such tools, it's essential to consider how they can optimize specific workflows, like content creation and SEO, to get the most out of them.

Фото профиля Magica
Magica1 месяц назад

@GeminiApp Paying for separate developer subscriptions every time a new version drops is getting old when Magica just bundles everything for $15.

Фото профиля Nitish Kumar Yadav
Nitish Kumar Yadav1 месяц назад

@GeminiApp Faster coding is useful. Fewer broken handoffs during long agent tasks would be the bigger win.

Фото профиля Deva
Deva1 месяц назад

@GeminiApp The real bottleneck for coding agents is token density in tool output. Unless the model is actually reasoning over compressed bash history or structured diffs, it just burns context on raw command noise. Ship the agent, but optimize the environment.

Фото профиля Nina Ledwinka
Nina Ledwinka1 месяц назад

@GeminiApp Our most intelligent workhorse model yet

Фото профиля Andrés
Andrés1 месяц назад

@GeminiApp Nice

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glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

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glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

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